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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/306222
Title: Survival analysis in credit scoring
Authors: Naidovich, Oleg
Nedzved, Alexander
Ye, Shiping
Keywords: ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
Issue Date: 2023
Publisher: Minsk : BSU
Citation: Pattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont : Proceedings of the 16 th International Conference, Belarus, Minsk, October 17–19, 2023 / Belarusian State University : eds. A. Nedzved, A. Belotserkovsky. – Minsk : BSU, 2023. – Pp. 158-161.
Abstract: In the domain of credit risk assessment, innovative approaches have emerged to address the challenge of predicting loan default probabilities. This article explores Survival Analysis, a statistical method capable of predicting the timing of loan repayments and distinguishing between completed repayments and unpaid loans, treating them as censored events. By integrating Survival Analysis, financial institutions can enhance their ability to forecast repayment timelines, minimize losses from non-performing loans, optimize cash flow management, refine credit collection strategies. The primary goal of this article is to investigate the utility of survival models in estimating Probability of Default (PD) and developing credit scorecards
URI: https://elib.bsu.by/handle/123456789/306222
ISBN: 978-985-881-522-6
Licence: info:eu-repo/semantics/openAccess
Appears in Collections:2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons

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